@inproceedings{riad-ullah-2025-syntaxmind,
    title = "{S}yntax{M}ind at {S}em{E}val-2025 Task 11: {BERT} Base Multi-label Emotion Detection Using Gated Recurrent Unit",
    author = "Riad, Md. Shihab Uddin  and
      Ullah, Mohammad Aman",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.semeval-1.191/",
    pages = "1450--1455",
    ISBN = "979-8-89176-273-2",
    abstract = "Emotions influence human behavior, speech, and expression, making their detection crucial in Natural Language Processing (NLP). While most prior research has focused on single-label emotion classification, real-world emotions are often multi-faceted. This paper describes our participation in SemEval-2025 Task 11, Track A (Multi-label Emotion Detection) and Track B (Emotion Intensity). We employed BERT as a feature extractor with stacked GRUs, which resulted in better stability and convergence. Our system was evaluated across 19 languages for Track A and 9 languages for Track B."
}